Update app.py

app.py

@@ -1,57 +1,112 @@
+import os
 import gradio as gr
 import requests
-import os
-from agent import run_agent_on_question
-from utils import get_hf_username, get_code_link
+import pandas as pd
+from agent import run_agent_on_question  # ✅ Import from your custom agent logic
+
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-API_BASE = "https://agents-course-unit4-scoring.hf.space"
+class BasicAgent:
+    def __init__(self):
+        print("✅ Custom Agent initialized.")
+    def __call__(self, question: str) -> str:
+        print(f"🧠 Agent received question: {question[:50]}...")
+        return run_agent_on_question({"question": question})
 
-def fetch_questions():
-    response = requests.get(f"{API_BASE}/questions")
-    if response.status_code == 200:
-        return response.json()
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    space_id = os.getenv("SPACE_ID")
+
+    if profile:
+        username = f"{profile.username}"
+        print(f"🔐 User logged in: {username}")
     else:
-        return []
+        print("❌ User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
+
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(f"📂 Agent code URL: {agent_code}")
+
+    # Instantiate agent
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        return f"❌ Error initializing agent: {e}", None
 
-def submit_answers(answers, username, code_link):
-    payload = {
-        "username": username,
-        "agent_code": code_link,
-        "answers": answers
+    # Fetch questions
+    try:
+        response = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+            return "⚠️ No questions fetched or invalid format.", None
+    except Exception as e:
+        return f"❌ Error fetching questions: {e}", None
+
+    # Run agent
+    results_log = []
+    answers_payload = []
+    for item in questions_data:
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or not question_text:
+            continue
+        try:
+            answer = agent(question_text)
+            answers_payload.append({
+                "task_id": task_id,
+                "submitted_answer": answer
+            })
+            results_log.append({
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": answer
+            })
+        except Exception as e:
+            results_log.append({
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": f"AGENT ERROR: {e}"
+            })
+
+    if not answers_payload:
+        return "❌ No answers submitted.", pd.DataFrame(results_log)
+
+    submission_data = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload
     }
-    response = requests.post(f"{API_BASE}/submit", json=payload)
-    if response.status_code == 200:
-        return response.json()
-    else:
-        return {"message": "Submission failed.", "score": 0}
-
-def run_and_submit():
-    print("Fetching questions...")
-    questions = fetch_questions()
-    print(f"Fetched {len(questions)} questions")
-
-    answers = []
-    for q in questions:
-        print(f"Running agent on task {q['task_id']}")
-        answer = run_agent_on_question(q)
-        answers.append({
-            "task_id": q["task_id"],
-            "submitted_answer": answer
-        })
-
-    username = get_hf_username()
-    code_link = get_code_link()
-    print(f"Submitting answers as {username} with code link {code_link}")
-
-    result = submit_answers(answers, username, code_link)
-    print(result)
-    return f"Score: {result.get('score', 0)}\nMessage: {result.get('message', 'No message')}"
 
+    try:
+        response = requests.post(f"{DEFAULT_API_URL}/submit", json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            f"✅ Submission Successful!\n"
+            f"User: {result_data.get('username')}\n"
+            f"Score: {result_data.get('score')}% "
+            f"({result_data.get('correct_count')}/{result_data.get('total_attempted')} correct)\n"
+            f"Message: {result_data.get('message')}"
+        )
+        return final_status, pd.DataFrame(results_log)
+    except Exception as e:
+        return f"❌ Submission Failed: {e}", pd.DataFrame(results_log)
+
+# ---- Gradio UI ----
 with gr.Blocks() as demo:
-    gr.Markdown("## GAIA Agent Evaluation Space")
-    with gr.Row():
-        submit_btn = gr.Button("Run Evaluation & Submit All Answers")
-    output = gr.Textbox(label="Submission Result")
-    submit_btn.click(fn=run_and_submit, outputs=output)
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("""
+    **Instructions:**
+    1. Log in to Hugging Face.
+    2. Click the button to evaluate your agent and submit answers.
+    ---
+    """)
+    gr.LoginButton()
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+    status_output = gr.Textbox(label="Submission Result", lines=5)
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
 
-demo.launch()
+if __name__ == "__main__":
+    print("🚀 Launching Agent Space...")
+    demo.launch(debug=True)